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Yisheng Zhong - One of the best experts on this subject based on the ideXlab platform.

  • Admissible Consensus analysis for high‐order linear singular swarm systems with multiple time‐varying delays and topology variances
    International Journal of Robust and Nonlinear Control, 2013
    Co-Authors: Zhicheng Yao, Guangbin Liu, Zhong Wang, Yisheng Zhong
    Abstract:

    SUMMARY Admissible Consensus analysis problems for high-order linear time-invariant singular swarm systems with multiple time delays and time-varying interaction topologies are investigated. First, necessary and sufficient conditions for admissible Consensus are presented, and admissible Consensus problems are transformed into admissible problems of multiple lower dimensional singular systems. Then, on the basis of the first equivalent form, explicit expressions of Consensus Functions are given, where the impacts of initial states of agents and protocols, time delays and topology variances are determined, respectively. Moreover, it is shown that if interaction topologies are balanced, then swarm systems with the same initial states but different interaction topologies and different time delays have an identical Consensus Function. Finally, numerical simulations are given to demonstrate theoretical results. Copyright © 2013 John Wiley & Sons, Ltd.

  • Stable-protocol output Consensus for high-order linear swarm systems with time-varying delays
    IET Control Theory & Applications, 2013
    Co-Authors: Guangbin Liu, Yisheng Zhong
    Abstract:

    Stable-protocol (SP) output Consensus analysis and design problems for high-order linear time-invariant swarm systems with time-varying delays are investigated. First, a dynamic output feedback Consensus protocol is proposed on the basis of the observable decomposition, and a necessary and sufficient condition for SP output Consensus is given, which transforms the SP output Consensus problem into asymptotical stability problems of multiple lower-dimensional subsystems. Then, in terms of linear matrix inequalities (LMIs), a sufficient condition for SP output consensualisation is presented, which can guarantee the scalability of swarm systems since it includes only five LMI constrains independent of the number of agents. Furthermore, an explicit expression of the output Consensus Function, which is independent of the time-varying delay, is shown and the impacts of initial states of agents and Consensus protocols on the output Consensus Function are determined, respectively. Finally, a numerical example is given to demonstrate theoretical results.

  • admissible Consensus and consensualization of high order linear time invariant singular swarm systems
    Physica A-statistical Mechanics and Its Applications, 2012
    Co-Authors: Jianxiang Xi, Yisheng Zhong
    Abstract:

    Admissible Consensus analysis and consensualizing controller design problems for high-order linear time-invariant singular swarm systems are investigated. Firstly, by projecting the state of a singular swarm system onto a Consensus subspace and a complement Consensus subspace, a necessary and sufficient condition for admissible Consensus is presented in terms of linear matrix inequalities (LMIs). An approach to decrease the calculation complexity is proposed, by which only three LMIs independent of the number of agents need to be checked. Then, by using the changing variable method, sufficient conditions for admissible consensualization are shown. An explicit expression of the Consensus Function is given, and it is shown that the modes of the Consensus Function can be arbitrarily placed if each agent is R-controllable and impulse controllable and the interaction topology has a spanning tree. Finally, theoretical results are applied to deal with cooperative control problems of multi-agent supporting systems.

  • Admissible consensualization for singular swarm systems with time delays
    2012
    Co-Authors: Fanlin Meng, Zongying Shi, Yisheng Zhong
    Abstract:

    Under dynamic output feedback Consensus protocols with time delays, admissible Consensus problems for highorder linear time-invariant singular swarm systems are investigated. The admissible Consensus problem is transformed into admissible problems of multiple singular subsystems by state decomposition. Linear matrix inequality (LMI) criterions for admissible consensualization are presented, which can guarantee scalability of swarm systems since they only involve four LMI constraints. Furthermore, an explicit expression of the Consensus Function is given, and the impacts of time delays, protocol states and interaction topologies on the Consensus Function are shown. Moreover, numerical sumulations are given to demonstrate theoretical results.

  • Consensus and Consensualization of High-Order Swarm Systems With Time Delays and External Disturbances
    Journal of Dynamic Systems Measurement and Control, 2012
    Co-Authors: Zongying Shi, Yisheng Zhong
    Abstract:

    By using dynamic output feedback Consensus protocols, Consensus analysis, and design, problems for swarm systems with external disturbances and time-varying delays are dealt with. First, two subspaces, namely, a Consensus subspace and a complement Consensus subspace, are defined. Based on the state projection onto the two subspaces, L2-Consensus and L2-consensualization problems are introduced. Then, a necessary and sufficient condition for Consensus is presented and an explicit expression of the Consensus Function is given. Especially, it is shown that the time-varying delay does not influence the Consensus Function. Finally, in terms of linear matrix inequalities, sufficient conditions for L2-Consensus and L2-consensualization are presented, respectively, which possess less calculation complexity, since they are independent of the number of agents, and numerical simulations are shown to demonstrate theoretical results.

Guangbin Liu - One of the best experts on this subject based on the ideXlab platform.

  • distributed admissible Consensus control for singular swarm systems with switching topologies
    International Journal of Robust and Nonlinear Control, 2015
    Co-Authors: Hao Liu, Xiaogang Yang, Zhicheng Yao, Guangbin Liu
    Abstract:

    Summary The interaction topologies of swarm systems may be time varying due to the motions of agents or the failure of communication equipment. The current paper focuses on admissible Consensus analysis and design problems for singular swarm systems with switching topologies. Admissible Consensus analysis problems are converted into admissible ones of a reduced-order switching subsystem by the state projection on the Consensus subspace and the complement Consensus subspace. Furthermore, an approach to determine the Consensus Function is proposed by the first equivalent form. Moreover, by the Riccati equation, admissible Consensus design criteria are presented, which can guarantee the scalability of singular swarm systems because they are independent of the number of agents. Finally, numerical examples are presented to check the correctness of theoretical results. Copyright © 2014 John Wiley & Sons, Ltd.

  • Uniqueness of Consensus Functions for time-delayed swarm systems with time-varying topologies
    Physica A: Statistical Mechanics and its Applications, 2015
    Co-Authors: Xiaogang Yang, Guangbin Liu
    Abstract:

    This paper shows that Consensus Functions for high-order linear swarm systems with time-varying interaction topologies and time delays are uniquely determined by initial states if interaction topologies are balanced. In addition, an explicit expression of the Consensus Function is presented, and the different influences of initial states of agents and protocols on the Consensus Function are determined respectively.

  • Admissible Consensus analysis for high‐order linear singular swarm systems with multiple time‐varying delays and topology variances
    International Journal of Robust and Nonlinear Control, 2013
    Co-Authors: Zhicheng Yao, Guangbin Liu, Zhong Wang, Yisheng Zhong
    Abstract:

    SUMMARY Admissible Consensus analysis problems for high-order linear time-invariant singular swarm systems with multiple time delays and time-varying interaction topologies are investigated. First, necessary and sufficient conditions for admissible Consensus are presented, and admissible Consensus problems are transformed into admissible problems of multiple lower dimensional singular systems. Then, on the basis of the first equivalent form, explicit expressions of Consensus Functions are given, where the impacts of initial states of agents and protocols, time delays and topology variances are determined, respectively. Moreover, it is shown that if interaction topologies are balanced, then swarm systems with the same initial states but different interaction topologies and different time delays have an identical Consensus Function. Finally, numerical simulations are given to demonstrate theoretical results. Copyright © 2013 John Wiley & Sons, Ltd.

  • Stable-protocol output Consensus for high-order linear swarm systems with time-varying delays
    IET Control Theory & Applications, 2013
    Co-Authors: Guangbin Liu, Yisheng Zhong
    Abstract:

    Stable-protocol (SP) output Consensus analysis and design problems for high-order linear time-invariant swarm systems with time-varying delays are investigated. First, a dynamic output feedback Consensus protocol is proposed on the basis of the observable decomposition, and a necessary and sufficient condition for SP output Consensus is given, which transforms the SP output Consensus problem into asymptotical stability problems of multiple lower-dimensional subsystems. Then, in terms of linear matrix inequalities (LMIs), a sufficient condition for SP output consensualisation is presented, which can guarantee the scalability of swarm systems since it includes only five LMI constrains independent of the number of agents. Furthermore, an explicit expression of the output Consensus Function, which is independent of the time-varying delay, is shown and the impacts of initial states of agents and Consensus protocols on the output Consensus Function are determined, respectively. Finally, a numerical example is given to demonstrate theoretical results.

Hamid Parvin - One of the best experts on this subject based on the ideXlab platform.

  • Social Network Optimization for Cluster Ensemble Selection.
    Fundamenta Informaticae, 2020
    Co-Authors: Chenyue Zhao, Hamid Parvin, Hosein Alizadeh, Behrouz Minaei, Majid Mohamadpoor, Mohammad Reza Mahmoudi
    Abstract:

    This paper studies the cluster ensemble selection problem for unsupervised learning. Given a large ensemble of clustering solutions, our goal is to select a subset of solutions to form a smaller yet better performing cluster ensemble than using all available solutions. The common way of aggregating the chosen solutions is accumulating the information of the selected results to a similarity matrix. This paper suggests transforming the similarity matrix to a modularity matrix and then applying a new Consensus Function which optimizes modularity measure in it. We represent the modularity maximization problem as a 0-1 quadratic program which can be exactly solved for small datasets. We also established a new greedy algorithm, namely sum linkage, to optimize the objective Function specially for large scale datasets in a very short time. We show that the proposed Consensus partition gets much closer to the actual cluster structure than the partitions obtained from the direct application of common cluster ensemble methods. The promising results compared with other most cited Consensus Functions show the excellent efficiency of the proposed method.

  • Consensus Function based on cluster-wise two level clustering
    Artificial Intelligence Review, 2020
    Co-Authors: Mohammad Reza Mahmoudi, Hamid Parvin, Samad Nejatian, Vahideh Rezaie, Hamidreza Akbarzadeh, Hamid Alinejad-rokny
    Abstract:

    The ensemble clustering tries to aggregate a number of basic clusterings with the aim of producing a more consistent, robust and well-performing Consensus clustering result. The current paper wants to introduce an ensemble clustering method. The proposed method, called Consensus Function based on two level clustering (CFTLC), introduces a new Consensus clustering where it makes a cluster clustering task through applying an average hierarchical clustering on a cluster–cluster similarity matrix obtained by an innovative similarity metric. By applying the average hierarchical clustering algorithm, a set of meta clusters has been attained. Considering each meta cluster as a Consensus cluster in the Consensus clustering output, it then assigns each data point to a meta cluster through defining an object-cluster similarity. Before doing anything, CFTLC converts the primary partitions into a binary cluster representation where the primary ensemble has been broken into a number of basic binary clusters (BC). CFTLC first combines the basic BCs with the maximum cluster–cluster similarity. This step is iterated as long as a predefined number of meta clusters are ready. At the subsequent step, it assigns each data point to exactly one meta cluster. The proposed method has been experimentally compared with the state of the art clustering algorithms in terms of accuracy and robustness.

  • Reliability-based fuzzy clustering ensemble
    Fuzzy Sets and Systems, 2020
    Co-Authors: Ali Bagherinia, Behrooz Minaei-bidgoli, Mehdi Hosseinzadeh, Hamid Parvin
    Abstract:

    Abstract In the clustering ensemble the quality of base-clusterings influences the Consensus clustering. Although some researches have been devoted to weighting the base-clustering, fuzzy cluster level weighting has been ignored, more specifically, they did not pay attention to the role of cluster reliability in the fuzzy clustering ensemble. In this paper, we propose a new fuzzy clustering ensemble framework without access to the features of data-objects based on fuzzy cluster-level weighting. The reliability of each fuzzy cluster is computed based on estimation of its unreliability, and is considered as its weight in the ensemble. The unreliability of fuzzy clusters is estimated by applying the similarity between fuzzy clusters in the ensemble based on an entropic criterion. In our framework, the final clustering is produced by two types of Consensus Functions: (1) a reliability-based weighted fuzzy co-association matrix is constructed from the base-clusterings and then, a single traditional clustering such as hierarchical agglomerative clustering or K-means is applied over the matrix to produce the final clustering. (2) a new graph based fuzzy Consensuses Function. The graph based Consensus Function has linear time complexity in the number of data-objects. Experimental results on various standard datasets demonstrated the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria and clustering robustness.

  • Clustering ensemble selection considering quality and diversity
    Artificial Intelligence Review, 2019
    Co-Authors: Sadr-olah Abbasi, Hamid Parvin, Samad Nejatian, Vahideh Rezaie, Karamolah Bagherifard
    Abstract:

    It is highly likely that there is a partition that is judged by a stability measure as a bad one while it contains one (or more) high quality cluster(s); and then it is totally neglected. So, inspiring from the evaluation of partitions, researchers turn to define measures for evaluation of clusters. Many stability measures have been proposed such as Normalized Mutual Information to validate a partition. The defined measures are based on Normalized Mutual Information. The drawback of the commonly used approach will be discussed in this paper and a criterion is proposed to assess the association between a cluster and a partition which is called Edited Normalized Mutual Information, ENMI criterion. The ENMI criterion compensates the drawback of the common Normalized Mutual Information (NMI) measure. Also, a clustering ensemble method that is based on aggregating a subset of primary clusters is proposed. The proposed method uses the Average ENMI as fitness measure to select a number of clusters. The clusters that satisfy a predefined threshold of the mentioned measure are selected to participate in the final ensemble. To combine the chosen clusters a set of Consensus Function methods are employed. One class of the used Consensus Functions is the co-association based Consensus Functions. Since the Evidence Accumulation Clustering, EAC, method can’t derive the co-association matrix from a subset of clusters, Extended EAC, EEAC, is employed to construct the co-association matrix from the chosen subset of clusters. The second class of the used Consensus Functions is based on hyper graph partitioning algorithms. The other class of the used Consensus Functions considers the chosen clusters as a new feature space and uses a simple clustering algorithm to extract the Consensus partitioning. The empirical studies show that the proposed method outperforms other well-known ensembles.

  • A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters
    Applied Intelligence, 2019
    Co-Authors: Musa Mojarad, Hamid Parvin, Samad Nejatian, Majid Mohammadpoor
    Abstract:

    For obtaining the more robust, novel, stable, and consistent clustering result, clustering ensemble has been emerged. There are two approaches in clustering ensemble frameworks: (a) the approaches that focus on creation or preparation of a suitable ensemble, called as ensemble creation approaches, and (b) the approaches that try to find a suitable final clustering (called also as Consensus clustering) out of a given ensemble, called as ensemble aggregation approaches. The first approaches try to solve ensemble creation problem. The second approaches try to solve aggregation problem. This paper tries to propose an ensemble aggregator, or a Consensus Function, called as Robust Clustering Ensemble based on Sampling and Cluster Clustering (RCESCC).RCESCC algorithm first generates an ensemble of fuzzy clusterings generated by the fuzzy c-means algorithm on subsampled data. Then, it obtains a cluster-cluster similarity matrix out of the fuzzy clusters. After that, it partitions the fuzzy clusters by applying a hierarchical clustering algorithm on the cluster-cluster similarity matrix. In the next phase, the RCESCC algorithm assigns the data points to merged clusters. The experimental results comparing with the state of the art clustering algorithms indicate the effectiveness of the RCESCC algorithm in terms of performance, speed and robustness.

Zhicheng Yao - One of the best experts on this subject based on the ideXlab platform.

  • distributed admissible Consensus control for singular swarm systems with switching topologies
    International Journal of Robust and Nonlinear Control, 2015
    Co-Authors: Hao Liu, Xiaogang Yang, Zhicheng Yao, Guangbin Liu
    Abstract:

    Summary The interaction topologies of swarm systems may be time varying due to the motions of agents or the failure of communication equipment. The current paper focuses on admissible Consensus analysis and design problems for singular swarm systems with switching topologies. Admissible Consensus analysis problems are converted into admissible ones of a reduced-order switching subsystem by the state projection on the Consensus subspace and the complement Consensus subspace. Furthermore, an approach to determine the Consensus Function is proposed by the first equivalent form. Moreover, by the Riccati equation, admissible Consensus design criteria are presented, which can guarantee the scalability of singular swarm systems because they are independent of the number of agents. Finally, numerical examples are presented to check the correctness of theoretical results. Copyright © 2014 John Wiley & Sons, Ltd.

  • Admissible Consensus analysis for high‐order linear singular swarm systems with multiple time‐varying delays and topology variances
    International Journal of Robust and Nonlinear Control, 2013
    Co-Authors: Zhicheng Yao, Guangbin Liu, Zhong Wang, Yisheng Zhong
    Abstract:

    SUMMARY Admissible Consensus analysis problems for high-order linear time-invariant singular swarm systems with multiple time delays and time-varying interaction topologies are investigated. First, necessary and sufficient conditions for admissible Consensus are presented, and admissible Consensus problems are transformed into admissible problems of multiple lower dimensional singular systems. Then, on the basis of the first equivalent form, explicit expressions of Consensus Functions are given, where the impacts of initial states of agents and protocols, time delays and topology variances are determined, respectively. Moreover, it is shown that if interaction topologies are balanced, then swarm systems with the same initial states but different interaction topologies and different time delays have an identical Consensus Function. Finally, numerical simulations are given to demonstrate theoretical results. Copyright © 2013 John Wiley & Sons, Ltd.

Zongying Shi - One of the best experts on this subject based on the ideXlab platform.

  • Admissible consensualization for singular swarm systems with time delays
    2012
    Co-Authors: Fanlin Meng, Zongying Shi, Yisheng Zhong
    Abstract:

    Under dynamic output feedback Consensus protocols with time delays, admissible Consensus problems for highorder linear time-invariant singular swarm systems are investigated. The admissible Consensus problem is transformed into admissible problems of multiple singular subsystems by state decomposition. Linear matrix inequality (LMI) criterions for admissible consensualization are presented, which can guarantee scalability of swarm systems since they only involve four LMI constraints. Furthermore, an explicit expression of the Consensus Function is given, and the impacts of time delays, protocol states and interaction topologies on the Consensus Function are shown. Moreover, numerical sumulations are given to demonstrate theoretical results.

  • Consensus and Consensualization of High-Order Swarm Systems With Time Delays and External Disturbances
    Journal of Dynamic Systems Measurement and Control, 2012
    Co-Authors: Zongying Shi, Yisheng Zhong
    Abstract:

    By using dynamic output feedback Consensus protocols, Consensus analysis, and design, problems for swarm systems with external disturbances and time-varying delays are dealt with. First, two subspaces, namely, a Consensus subspace and a complement Consensus subspace, are defined. Based on the state projection onto the two subspaces, L2-Consensus and L2-consensualization problems are introduced. Then, a necessary and sufficient condition for Consensus is presented and an explicit expression of the Consensus Function is given. Especially, it is shown that the time-varying delay does not influence the Consensus Function. Finally, in terms of linear matrix inequalities, sufficient conditions for L2-Consensus and L2-consensualization are presented, respectively, which possess less calculation complexity, since they are independent of the number of agents, and numerical simulations are shown to demonstrate theoretical results.

  • Delay-dependent admissible consensualization for singular time-delayed swarm systems ☆
    Systems & Control Letters, 2012
    Co-Authors: Fanlin Meng, Zongying Shi, Yisheng Zhong
    Abstract:

    Abstract Admissible Consensus analysis and design problems for high-order linear time-invariant singular swarm systems with time delays are investigated. Firstly, by state decomposition, the admissible Consensus problem is transformed into admissible problems of multiple singular subsystems with lower dimensions. Then, linear matrix inequality (LMI) criteria for admissible consensualization are presented, which only involve eight LMI constraints independent of the number of agents. Moreover, an explicit expression of the Consensus Function which is independent of time delays is presented, and the impacts of protocol states and interaction topologies on the Consensus Function are revealed. Finally, a numerical example is given to illustrate the effectiveness of theoretical results.

  • Output Consensus for high-order linear time-invariant swarm systems
    International Journal of Control, 2012
    Co-Authors: Zongying Shi, Yisheng Zhong
    Abstract:

    Output Consensus analysis and design problems of high-order linear time-invariant swarm systems are investigated. First, an output Consensus subspace and a complement output Consensus subspace are introduced. By output projection onto the two subspaces and the partial stability theory, a necessary and sufficient condition for output Consensus is presented, and an explicit expression of the output Consensus Function, which is jointly determined by the interaction topology and initial states of all agents, is given. Especially, it is shown that the output Consensus Function is completely determined by initial states of all agents if the interaction topology is balanced. Then, an approach to determine gain matrices in Consensus protocols of swarm systems is proposed, which has less calculation complexity. Finally, a numerical example is shown to demonstrate theoretical results.